SOURCE-LINKED INTELLIGENCE
An Analytical Theory of Auxiliary Learning
Auxiliary learning is an optimization paradigm in which a neural network's performance on a target task is improved by jointly training it on additional tasks. However, the mechanisms behind this improvement remain poorly understood. We study this problem using a teacher-student framework and derive a closed system of differential equations describing the dynamics of online stochastic gradient descent in the large-input limit. For linear networks, we obtain a closed-form expression for the generalization error to leading order in the learning rate, quantifying how task correlations and label n
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-24T13:18:13.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.